Mach Industries, a rapidly growing defense technology company founded in 2022, is seeking a Machine Learning Engineer to own and scale the training, data, and edge-inference backbone of our AI-forward autonomy stack. The role involves building data and training infrastructure for flight/simulation/HITL data, enabling real-time on-embedded hardware inference, and collaborating across perception, localization, embedded, and flight-test teams from prototype to deployment. Responsibilities include: designing end-to-end ML data pipelines (data ingestion, labeling/QA workflows, augmentation, dataset versioning), standing up and scaling training/eval infrastructure (distributed multi-GPU training, experiment tracking, model registry, CI-based evaluation with regression gates and retraining loops); deploying and optimizing models for real-time edge inference on Jetson-class hardware (quantization/pruning, TensorRT/ONNX Runtime) with attention to latency, throughput, and SWaP; building and improving models for detection, segmentation, tracking, search, and multi-sensor fusion for EO/IR and auxiliary sensing; generating synthetic data at scale (simulation + domain randomization) to cover long-tail and degraded conditions; instrumenting runtime health, drift detection, and performance metrics; providing field-data visualization and root-cause tooling to accelerate updates; partnering with other autonomy disciplines to move capabilities from prototype to HITL to flight to deployment. Required qualifications include strong generalist software engineering (Python ML tooling, production C

Mach Industries, a rapidly growing defense technology company founded in 2022, is seeking a Machine Learning Engineer to own and scale the training, data, and edge-inference backbone of our AI-forward autonomy stack. The role involves building data and training infrastructure for flight/simulation/HITL data, enabling real-time on-embedded hardware inference, and collaborating across perception, localization, embedded, and flight-test teams from prototype to deployment. Responsibilities include: designing end-to-end ML data pipelines (data ingestion, labeling/QA workflows, augmentation, dataset versioning), standing up and scaling training/eval infrastructure (distributed multi-GPU training, experiment tracking, model registry, CI-based evaluation with regression gates and retraining loops); deploying and optimizing models for real-time edge inference on Jetson-class hardware (quantization/pruning, TensorRT/ONNX Runtime) with attention to latency, throughput, and SWaP; building and improving models for detection, segmentation, tracking, search, and multi-sensor fusion for EO/IR and auxiliary sensing; generating synthetic data at scale (simulation + domain randomization) to cover long-tail and degraded conditions; instrumenting runtime health, drift detection, and performance metrics; providing field-data visualization and root-cause tooling to accelerate updates; partnering with other autonomy disciplines to move capabilities from prototype to HITL to flight to deployment. Required qualifications include strong generalist software engineering (Python ML tooling, production C